模糊神经网络

运用模糊神经网络方法和神经网络方法（NN）进行混凝土强度预测和配合比优化设计；

The approaches of FNN and artificial neural network ( NN ) are applied to predict strength and to design the mix proportion of concrete .

基于模糊神经网络和数据融合的结构裂纹故障诊断

Fault diagnosis to structural crack based on FNN and Data Fusion

B样条隶属函数的模糊神经网络研究

Research on Neural Fuzzy Network Using B Spline Membership function

基于模糊神经网络的Agent反应器结构及实现

Architecture and Realization of Agent Reactive Module Based on Fuzzy Neural Networks

文中针对旋转机械几种典型故障的诊断，将模糊神经网络方法与传统BP网络方法和模糊分类方法进行了比较。

Several typical faults in rotating machinery are analyzed with the method .

基于H∞变结构的不确定机器人模糊神经网络控制

Fuzzy neural-network control based on H_ ∞ variable structure control for uncertain robot manipulators

基于TS模糊神经网络的Fuzzy规则自动获取研究

Research of automatic fuzzy rule extraction based on TS fuzzy neural networks

因此本设计选择B样条函数作为模糊神经网络控制器的隶属函数，以B样条函数为媒介将模糊控制与神经网络有机地结合起来，发挥各自优势，实现对控制器的优化。

So by combination of fuzzy control and neural-networks with B-Spline functions , can advance both advantages to control the system .

然后，再运用BP算法优化模糊神经网络的连接权系数。

Finally , the back propagation algorithm was used to optimize the connection coefficients of fuzzy neural network .

重点介绍了模糊神经网络PID算法在PLC控制器上的仿真实现方法。

The paper is focus on the method of PLC simulation for RBF fuzzy neural network PID algorithm .

首先，使用遗传算法和BP算法相结合的方式来优化模糊神经网络；

Firstly , the hybrid learning methods integrating genetic algorithm with BP algorithm to optimize fuzzy-neural network are employed .

然后采用BP算法对神经网络进行调节，从而确定出模糊神经网络的参数。

Then adjusted the neural network by Back-Propagation ( BP ) algorithm , so the parameters of fuzzy neural network were determined .

提出了将基于模糊神经网络的PID控制策略用于电动助力转向系统中助力电机的控制。设计了电动助力转向试验台，并进行了电动助力转向系统的台架试验。

A PDI control strategy based on fuzzy neural network is applied to the control of motor in electric power steering system .

该系统利用模糊神经网络调整PID参数，进一步完善了PID控制的自适应性能。

Fuzzy_Neural Network is used to modulate PID parameters in this system , which further perfects the adaptive performance of PID control .

单体模糊神经网络：在智能控制中的应用及VLSI实现

Monolithic Fuzzy Neural Network ( MFNNs ): Application in Intelligent Control & VLSI Realization

基于自学习模糊神经网络AMT车辆巡航控制

Investigation on AMT vehicle cruise control using self-lear-ning fuzzy neural networks

混沌机制在T-S模型模糊神经网络的系统辨识研究

Study on Chaotic Mechanism in System Identification Using T-S Model Fuzzy Neural Networks

基于Chebyshev基函数模糊神经网络的快速辨识方法

Fast Identification Method of Fuzzy Neural Networks Based on Chebyshev Basis Function

永磁电机采用模糊神经网络和无位置传感器观测器的控制方法，利用数字信号处理器（DSP）实现运动过程的控制。

The neuro-fuzzy scheme and position-sensorless control of the permanent-magnet motor is investigated , and the digital signal process ( DSP ) controls its movement .

一种改进型T-S模糊神经网络

An Improved T-S Fuzzy Neural Network

介绍了利用模糊神经网络建立T-S模糊模型的方法；

A fuzzy modeling method based on a fuzzy neural network of T S model is introduced .

导出故障原因计算模型和故障诊断数学模型，并采用改进的5层BP模糊神经网络求解。

Calculation model of fault causation and mathematical model of fault diagnosis was educed , these models were calculated with improved BP fuzzy neural network with 5 layer .

提出基于T-S模糊神经网络的变结构控制方法。

A variable structure control method based on T-S fuzzy neural network ( FNN ) is brought forward .

首先提出一种基于Agent封装的模糊神经网络结构，它能主动发现服务并进行自发互操作，多Agent之间能相互协调和协同工作；

A kind of new belief measure structure , called fuzzy-neural network structure under the encapsulation of agent is presented , which can discover service proactively and supply spontaneous interoperation in cooperation with multi-agents .

ANFIS实现的模糊神经网络在交通信号配时优化中的应用

Fuzzy Neural Network Realized by ANFIS and Its Application to the Traffic Signal Timing Optimization

利用GA-BP算法对模糊神经网络进行优化

Optimizing Fuzzy Neural Network Using GA-BP Algorithm

利用模糊神经网络避障控制器融合CCD摄像机与超声波传感器探测到的环境信息，以实现机器人的安全避障。

In order to avoid the obstacles successfully , detection results from CCD and ultrasonic sensors are fused by a fuzzy neural network , which acts as an avoidance controller .

考虑了模糊神经网络的学习功能，提出利用Additivemultiplicative模糊神经网络（AMFNN）对ATM网络进行拥塞控制的方案。

Based on additive-multiplicative fuzzy neural network ( AMFNN ), a novel congestion control scheme for ATM network is presented .

提出了一种宽带网络中基于模糊神经网络的连接接纳控制（）方法，结合了模糊逻辑的语言控制能力和神经网络的自学习能CAC力。

This paper proposes a fuzzy neural network approach for connection admission control ( FNN-CAC ) in broadband networks .

本文采用模糊神经网络算法对ATM网络进行连接接纳控制，仿真结果表明它比传统的算法有更好的效果。

This paper brings the fuzzy neural network algorithms into the call admission control in ATM networks . The simulation result shows that it has better effect than usual algorithms .